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Difficulty: Very hardPlanning Compute Engine Resources and Machine Types

An enterprise organization is migrating a legacy batch processing and simulation application to Google Cloud Compute Engine. The application operates continuously 24/7 as a steady-state workload and requires a non-standard resource ratio of 1 vCPU to 8 GB of RAM to prevent paying for unneeded compute processing power. Furthermore, the application performs synchronous state writes to attached disks and cannot tolerate abrupt or ungraceful instance terminations. The infrastructure team must minimize total cost of ownership over a 3-year horizon while ensuring full operational stability and exact resource fit. Which compute planning strategy best meets these requirements?

  1. Provision N2 custom machine types configured with a 1 vCPU to 8 GB RAM ratio, and purchase 3-year resource-based Committed Use Discounts (CUDs) for the baseline vCPU and memory capacity.Answer
  2. B
    Provision Spot VMs configured with N2 custom machine types with 1 vCPU to 8 GB RAM to leverage up to 90% cost savings compared to standard compute pricing.
  3. C
    Migrate the workload to a Google Kubernetes Engine (GKE) Autopilot cluster and configure custom node pools with high-memory machine configurations under 3-year Committed Use Discounts.
  4. D
    Deploy the application onto Cloud Run as a long-running service with custom CPU and memory limits, applying Sustained Use Discounts to optimize 24/7 execution costs.

Answer

Provision N2 custom machine types configured with a 1 vCPU to 8 GB RAM ratio, and purchase 3-year resource-based Committed Use Discounts (CUDs) for the baseline vCPU and memory capacity.
The correct strategy leverages N2 custom machine types to precisely fit the required 1 vCPU to 8 GB RAM ratio without over-provisioning unused vCPUs. Pairing this configuration with 3-year resource-based Committed Use Discounts (CUDs) yields the lowest cost for a steady-state 24/7 workload while guaranteeing VM availability and persistent disk statefulness.

Step-by-Step Solution

1
Analyze workload resource ratio requirements.
Standard predefined machine types offer specific vCPU-to-RAM ratios (e.g., standard is 1:4, highmem is 1:8). Custom machine types (such as N2 custom) permit precise specification of vCPU and RAM allocations to eliminate wasted compute costs.
Matching exact resource needs requires custom machine configuration when standard ratios do not fit.
2
Evaluate fault tolerance and availability needs.
The application is stateful and cannot sustain ungraceful terminations, which disqualifies Spot/Preemptible VMs despite their deep discounts.
Spot instances offer no availability SLA and can be reclaimed with a 30-second notification.
3
Determine optimal pricing model for 24/7 steady-state long-term execution.
For a 24/7 baseline workload running continuously over a 3-year horizon, purchasing resource-based Committed Use Discounts (CUDs) provides the highest guaranteed discount rate.
CUDs commit to a specific vCPU and memory footprint over 1 or 3 years in exchange for significant cost reductions without operational risk.

Key Concept

Compute Engine Resource Planning and Machine Type Customization
Estimated Time:2m 0s
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Topics

Planning Compute Engine Resources and Machine Types

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